Solving the Cold Start Problem in Context-Aware Recommender Systems using Real-Time Emotional Input

Integrating time facial emotion recognition with hybrid recommendation algorithms really helps with the Cold Start Problem. In the future we can try adding signals like heart rate and temperature to create multi-modal recommendation systems. We checked how well our system worked based on how accurate our predictions were and how satisfied users were. ·                  Detection Accuracy: Our model was about 92% of the time.This accuracy rate shows that our facial emotion recognition works well. ·                  Convergence: Our models training and validation losses went down a lot over time which means it learned at a pace.The models learning rate seemed stable which is good for the CNN. ·                  The "Neutrality" Bias: We noticed that our model sometimes got confused between emotions like slight smiles and neutral expressions.This is an issue with facial emotion recognition.The Cold Start Problem is a challenge in recommendation systems. Our approach helps solve it. ·                  Environmental Sensitivity: Real-time conditions, including ambient lighting and webcam resolution, were found to impact the reliability of the feature extraction.

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